Monitoring trends of technological changes based on the dynamic patent lattice: A modified formal concept analysis approach
Research Highlights
► Proposing a systematic approach to monitoring trends of technological changes. ► Developing dynamic patent lattice to visualize relationships among patents over time. ► Verifying advantages of our method in terms of visualization and explanatory power.
Introduction
The ever faster pace and increasing complexity of technological innovation places more emphasis on strategic importance of monitoring technological changes. In this situation, firms are focusing increasing attention on technology monitoring – efforts to observe and assess technologies – to gain and maintain a competitive edge [1], [2]. Technology monitoring is a general term that concerns acquisition, assessment, and communication of information on technology, and has been defined in many different ways. According to the European Industrial Research Management Association, it was referred to as identification and assessment of technological changes that are critical to the firm's competitive position [3]. It was defined as scanning the environment to obtain historical information on technology's development, current information of the state of art today, and information pointing directly to future prospects [4]. Although variations may exist among researchers regarding to the definition and scope of technology monitoring, the literature commonly views technology monitoring as an indispensible task in defending against the potential threats and exploiting the promising opportunities [5].
Technological changes and the process of innovation have been regarded as an evolutionary process having a certain inner logic of its own and depending on manifold factors of selection environment [6], [7], [8]. A variety of concepts, such as technological regime, technological trajectory, Abernathy and Utterback (AU) model, reverse salient, lock-in, dominant design, etc., have been put forward to capture the common features of technological changes. While previous studies are useful for understanding important aspects of technological changes, a lacuna still remains in the literature as to how to monitor trends of technological changes in a systematic and quantitative manner for the following reasons. First of all, previous models cannot provide objective information about technological changes based on objective technological data since most research has focused on case-based conceptual framework [9]. Even though many methods such as statistical analysis and trend extrapolation have been applied to indirect measures of technological changes to enhance the objectivity of analysis results, it can only describe overall directions and processes of technological changes. To provide more detailed guidelines for trends of technological changes, it is critical to secure the applicable quantitative data and provide the objective information. Second, despite of idiosyncratic nature of technological changes and difficulties in generalization thereof, most previous studies have attempted to analyze the technological changes at the macro level. As a result, the explanatory power of framework lies mainly in the general patterns of technological changes, and less in the trends of changes within a particular technology field [10]. Consequently, recent years have seen major increases in attempts to develop models, methods, and tools to overcome the limitations mentioned above.
In this respect, patent analysis has long been considered as a useful analytic tool, and significantly benefited from the use of computerized methods such as text mining and bibliometric analysis. An analysis of technological information in patent documents is visualized as a patent map, allowing the complex information to be understood easily and effectively [11]. Patent maps can highlight the crucial pieces of knowledge about technological details and relationship, novel industrial solutions [12], business trends [13], competitive positions [14], [15], and infringement risk [16]. Among the various methods and visualization techniques for analyzing patent information, patent citation analysis has been the most frequently adopted tool. The citation information has been utilized to investigate knowledge flows at various levels, such as national [17], industry [18], firm [19], and technology level [20]. In addition to simple frequency of citations, such quantitative indexes as citing-cited intensity and linkage, technological prominence, technological coverage, and technology cycle time have been developed for this purpose. However, albeit easy to understand and simple to use, the salient problems and deficiencies of patent citation analysis for technology monitoring have been pointed out, as clarified next [21]. First and foremost, the scope of analysis and richness of potential information are limited because it only takes into account citing-cited information. Although the citing-cited information has been employed as a proxy for technological knowledge flow and technological prominence, it cannot consider the internal technological relationships among patents. Thus, what has been technologically changed among patents cannot be fully captured from patent citation analysis. Second, it is difficult to grasp up-to-date trends of technological changes since the time lags between citing and cited patents are more than ten years on average [22]. For this reason, patent citation analysis is forced to face a serious challenge in monitoring the recent trends of technological changes, especially as for fast changing and complex technology fields. To overcome the limitations mentioned above, the keyword-based patent analysis has been suggested as a remedy of patent citation analysis. However, despite all the possibilities offered by the keyword-based patent analysis, it is still subject to some limitations that need to be further addressed. Initially, only simple and static methods have been utilized such as cluster and co-word analysis incapable of investigating trends of technological changes over time. Secondly, regarding to the first problem, the keyword-based patent map only shows the relations of technologies without time considerations. Finally, it is difficult to interpret and understand the detailed changes in technology due to large and complex structures of patent maps. Both the limitations of patent citation analysis and keyword-based patent analysis will be fully addressed in our proposed analysis model.
The primary purpose of this study is to propose a formal concept analysis (FCA)-based approach to developing a dynamic patent lattice that can analyze the complex relations among patents and monitor the trends of technological changes over time. The FCA is a promising mathematical tool for grouping objects with shared properties based on the lattice theory. The distinct strengths of FCA, vis-á-vis other methods, lie in structuring and displaying the relations among objects in voluminous data. For the purpose of technology monitoring, the FCA is extended to take into account time periods and changes keywords of patents. A patent context is first constructed with the aid of domain experts and text mining technique. Two types of dynamic patent lattices are then developed by executing the modified FCA algorithm. Based on the dynamic patent lattice, in-depth analysis is carried out to obtain richer information on technological changes. It has been recognized that the cornerstone of technology monitoring process is to identify historical information on technology's development [23]. In this regard, we believe that the suggested approach can improve the efficiency of technology monitoring process by systemizing experts' manual work and complement other technology monitoring methods.
The rest of this paper is organized as follows. As an introductory statement, the general background of patent analysis and FCA is reviewed in Section 2. The proposed FCA-based approach is explained in Section 3, and illustrated with a case study of the laser technology in lithography for semiconductor manufacturing in Section 4. Finally, this paper ends with conclusions in Section 5.
Section snippets
Background
Put theoretically, our attempt is to integrate the modified FCA algorithm together with patent analysis under a systematic framework. They are used together only rarely, and thus most readers will be comfortable with one or some, but perhaps not all of them. We therefore touch briefly on what they are and how they are combined in this study.
Proposed approach
In this section, we examine the overall process of proposed approach, giving a brief explanation of each stage at the same time. The proposed approach is composed of five stages, as depicted in Fig. 2. First of all, data collection and data preprocessing are the preliminary step. A technology field of interests is selected and related patents are collected in electronic text format. Second, a patent database is constructed by parsing the patent documents. The original documents are expressed in
Case study
A case study of patents related to the laser technology in lithography for semiconductor manufacturing is presented to illustrate the suggested approach. We consider this case example appropriate for the following reasons. First, the laser technology is one of the most critical technologies in the lithography process for semiconductor manufacturing. Second, the performance improvement has been continuously introduced to keep pace with shrinking feature sizes as well as light sources, as
Conclusions
This article presents a modified FCA-based dynamic patent lattice that can analyze the complex relations among patents and monitor trends of technological changes. The FCA is extended to take time periods and changes of patent keywords into account for the purpose of technology monitoring. A patent context is first constructed with the aid of domain experts and text mining technique. Two types of dynamic patent lattice are then developed by executing a modified FCA algorithm. The proposed
Acknowledgment
This work was supported by the Mid-career Researcher Program through NRF grant funded by the MEST (No. 2009-0085757).
Changyong Lee is a Ph.D. candidate in the Department of Industrial Engineering at Seoul National University (SNU). He holds a BS in computer science from Korea Advanced Institute of Science and Technology (KAIST). His research interests are in the areas of technology intelligence, patent analysis, business and technology planning, and service engineering.
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Changyong Lee is a Ph.D. candidate in the Department of Industrial Engineering at Seoul National University (SNU). He holds a BS in computer science from Korea Advanced Institute of Science and Technology (KAIST). His research interests are in the areas of technology intelligence, patent analysis, business and technology planning, and service engineering.
Jeonghwan Jeon is a Ph.D. candidate in the Department of Industrial Engineering of SNU. He holds BS and MS in mechanical engineering from KAIST. He has nine years of experience as a senior engineer at semiconductor division of Samsung Electronics, Korea. His research interests include technology roadmap, technology planning, and performance evaluation.
Yongtae Park is a professor in the Department of Industrial Engineering at SNU. He holds a BS in industrial engineering from SNU, and an MS and PhD in operations management, both from the University of Wisconsin-Madison. His research topics cover a wide variety of areas including technology management, technological innovation, knowledge management, and service engineering.